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作 者:陶经辉[1,2] 郭小伟[1] TAO Jing-hui;GUO Xiao-wei(School of Marketing and Logistics Management,Nanjing University of Finance &Economics,Nanjing210046,China;Jiangsu Key Laboratory of Modern Logistics(Nanjing University of Finance &Economics),Nanjing210046,China)
机构地区:[1]南京财经大学营销与物流管理学院,江苏南京210023 [2]江苏省现代物流重点实验室(南京财经大学),江苏南京210023
出 处:《中国管理科学》2018年第12期124-134,共11页Chinese Journal of Management Science
基 金:国家社会科学基金资助项目(14BGL173);江苏高校哲学社会科学研究重点项目(2016ZDIXM024);江苏高校优势学科建设工程资助项目(PAPD)
摘 要:随着经济全球化的不断深化,世界各国越来越重视通过构建和延伸产业链,通过区域之间的产业联动发展等方式,来获得更多的经济要素、占领更大的市场和实现产业转型升级,以达到提高整个区域产业竞争力的目的,物流园区和产业园区的协同选址以及货物的优化分配方案有利于加强区域产业间的协同效应,从而促进区域经济的整体发展。本文在物流园区和产业园区备选点确定的前提下,针对物流园区和产业园区协同选址问题,采用多目标理论,分别以总成本和总废气排放量为目标函数,建立多目标选址模型,并设计了带有精英策略的非支配排序遗传算法,通过算法求解物流园区和产业园区的协同建设地址以及货物优化分配方案。应用算例对算法进行了分析,结果表明本文设计的算法具有一定的高效性,同时通过对主要参数的敏感性分析,验证了模型具有稳定性。With the deepening of economic globalization, countries all over the world pay more and more attention to build and extend the industrial chain, obtain more economic elements, occupy a larger market and achieve industrial transformation and upgrading through the industry linkage development between the regions, in order to reach the purpose of improving the regional industry competitiveness. The co-location of logistics park and industrial park, as well as optimal allocation of the goods, is beneficial to strengthen synergies between the regional industry, so as to promote the overall development of regional economy. Under the premise of an alternative point being determined, the theory of multiple objective with total costs and total carbonemissions being used as objective function according to co-location problem of logistics park and industrial park, the multi-objective location model is established, and the non-dominated sorting genetic algorithm Ⅱ is designed, thought the algorithm to solve the logistics park and industrial park of collaborative construction address and optimized allocation of goods. The algorithm is analyzed by an example, and the results show that the proposed algorithm is highly efficient, and the stability of the model is verified by sensitivity analysis of the main parameters.
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